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Building on HF
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CYGDEN
CYGDEN
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CYGDEN
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Run open-source models with up to 120B parameters locally on your Mac! https://youtu.be/Ql4PDjoxNXQ?si=3yHpz51uinUjgyNh
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Can you teach a giant like Google's Gemini to protect user privacy? A new step-by-step guide shows that the answer is a resounding "yes." While powerful, large language models aren't specialized for privacy tasks. This tutorial by Analytics Vidhya walks through how to fine-tune Gemini into a dedicated tool for PII anonymization. To teach the model this critical skill, the author needed a robust dataset with thousands of clear 'before' and 'after' examples. We're thrilled they chose the Ai4Privacy pii-masking-200k dataset for this task. Our data provided the high-quality, paired examples of masked and unmasked text necessary to effectively train Gemini to identify and hide sensitive information accurately. This is a perfect example of how the community can use open-source data to add a crucial layer of safety to the world's most powerful models. Great work! 🔗 Check out the full tutorial here: https://www.analyticsvidhya.com/blog/2024/03/guide-to-fine-tuning-gemini-for-masking-pii-data/ 🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/ #DataPrivacy #AI #LLM #FineTuning #Anonymization #GoogleGemini #Ai4Privacy #World's largest open privacy masking dataset
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